Adaptation method for inter-person biometrics variability

ABSTRACT

Embodiments of a system and method for verifying an identity of a claimant are described. In accordance with one embodiment, a feature may be extracted from a biometric sample captured from a claimant claiming an identity. The extracted feature may be compared to a template associated with the identity to determine the similarity between the extracted feature and the template with the similarity between them being represented by a score. A determination may be made to determine whether the identity has a correction factor associated therewith. If the identity is determined to have a correction factor associated therewith, then the score may be modified using the correction factor. The score may then be compared to a threshold to determine whether to accept the claimant as the identity. In accordance with a further embodiment, during enrollment of a subject in a biometric verification system, a feature may be extracted from a biometric sample captured from the subject requesting enrollment and a standard deviation for the feature may then be calculated. A determination may then be performed to determining whether the standard deviation of the feature is greater than a standard deviation of a centroid of a density function. If the standard deviation of the feature is greater than the standard deviation of the centroid, then a correction factor for the subject may be derived based on a trend line of the density function.

TECHNICAL FIELD

Embodiments described herein relate generally to speech recognition andmore particularly relate to speaker verification.

BACKGROUND

Biometrics is the science and technology of measuring and statisticallyanalyzing biological data. A biometric is a measurable, physicalcharacteristic or personal behavioral trait used to recognize theidentity, or verify the claimed identity, of an enrollee. In general,biometrics statistically measure certain human anatomical andphysiological traits that are unique to an individual. Examples ofbiometrics include fingerprints, retinal scans, hand recognition,signature recognition, and speaker recognition.

Verification (also known as authentication) is a process of verifyingthe user is who they claim to be. A goal of verification is to determineif the user is the authentic enrolled user or an impostor. Generally,verification includes four stages: capturing input; filtering unwantedinput such as noise; transforming the input to extract a set of featurevectors; generating a statistical representation of the feature vector;and performing a comparison against information previously gatheredduring an enrollment procedure.

Speaker verification systems (also known as voice verification systems)attempt to match a voice of a speaker whose identity is undergoingverification with a known voice. Speaker verification systems help toprovide a means for ensuring secure access by using speech utterances.Verbal submission of a word or phrase or simply a sample of anindividual speaker's speaking of a randomly selected word or phrase areprovided by a claimant when seeking access to pass through a speakerrecognition and/or speaker verification system. An authentic claimant isone whose utterance matches known characteristics associated with theclaimed identity.

To train a speaker verification system, a claimant typically provides aspeech sample or speech utterance that is scored against a modelcorresponding to the claimant's claimed identity and a claimant score isthen computed to confirm that the claimant is in fact the claimedidentity.

There exist groups of users that have unstable or unreliable biometricdata that cause biometric systems to falsely reject them. These userswith unstable or unreliable biometric data may be referred to as“goats.” Implementation of biometric systems capable of providingincreased acceptance rates for such users could be advantageous.

SUMMARY

Embodiments of a system and method for verifying an identity of aclaimant are described. In accordance with one embodiment, a feature maybe extracted from a biometric sample captured from a claimant claimingan identity. The extracted feature may be compared to a templateassociated with the identity to determine the similarity between theextracted feature and the template with the similarity between thembeing represented by a score. A determination may be made to determinewhether the identity has a correction factor associated therewith. Ifthe identity is determined to have a correction factor associatedtherewith, then the score may be modified using the correction factor.The score may then be compared to a threshold to determine whether toaccept the claimant as the identity.

In another embodiment, the biometric sample may comprise a speech samplespoken by the claimant. In one embodiment, the correction factor may bederived from a biometric sample captured from the claimed identity. Thecorrection factor may also be retrieved from a correction factor datastore. To modify the score, the correction factor may be added to thescore. In one embodiment, the correction factor may have a negativevalue. In another embodiment, the claimant may be rejected if the scoreof the claimant is determined to exceed the threshold.

In accordance with a further embodiment, during enrollment of a subjectin a biometric verification system, a feature may be extracted from abiometric sample captured from the subject requesting enrollment and astandard deviation for the feature may then be calculated. Adetermination may then be performed to determining whether the standarddeviation of the feature is greater than a standard deviation of acentroid of a density function. If the standard deviation of the featureis greater than the standard deviation of the centroid, then acorrection factor for the subject may be derived based on a trend lineof the density function.

In one embodiment, the correction factor may be stored in correctiondata store. In another embodiment, the density function may comprise amatch score vs. standard deviation density function of a samplepopulation. In a further embodiment, the correction factor is furtherderived from the difference between a mean score obtained from thecentroid and a score for the subject derived from the trend line. As afurther option, a determination may be performed to determining whetherthe standard deviation of the feature exceeds a threshold value. If thestandard deviation of the feature is determined to exceed the thresholdvalue, then the threshold value may be used in place of the standarddeviation of the feature to derive the correction factor for thesubject.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a density function graph for an exemplary population wherematch scores are plotted against standard deviations of each sample inthe exemplary population in accordance with an illustrative embodiment;

FIG. 2 is a density function graph of an exemplary population withvarious trend lines applied thereto in accordance with an illustrativeembodiment;

FIG. 3 is flowchart of a process for training a biometric system inaccordance with an exemplary embodiment;

FIG. 4 is a flowchart of a process for deriving a correction factor inaccordance with an exemplary embodiment;

FIG. 5 is a flowchart of a verification process in which a correctionfactor may be applied in accordance with an exemplary embodiment;

FIG. 6 is a density function graph illustrating an effect on decisionmaking of the application of a correction factor to a match score in abiometric verification system in accordance with an exemplaryembodiment; and

FIG. 7 is a schematic diagram of an illustrative hardware environment inaccordance with an exemplary embodiment.

DETAILED DESCRIPTION

For most biometrics, a small percentage of people exhibit biometricsfeatures that are unreliable for use in biometric verification oridentification. For example, older people often tend to have very lightfingerprints that are unsuitable for a fingerprint based biometricsystem. As another example, a person may have a medical condition thatmakes their voice unstable and therefore unsuitable for a speech basedbiometric system. Embodiments described herein may be implemented forhelping to adapt a biometric system to such users without compromisingthe overall accuracy of the biometric system's decision algorithm.

More particularly, biometric feature vectors derived from a biometricsenrollment process can be used to determine the variance in anenrollee's (i.e., a user) biometrics. During enrollment, a relationshipbetween a user's biometric features (i.e., feature vectors) and thereliability of a given biometric system can be used to identify suchusers/enrollees that have features that are unreliable for the givenbiometric system. During verification, the biometric match-score of anenrollee with unreliable features may be assigned a small correction tohelp allow subsequent correct verification of the user in question.These corrections can be computed during enrollment.

Such a mechanism helps to afford the reliably use of a biometrics systemby users that would otherwise be unreliable for the biometric system.Since embodiments of the adaptation process may be implemented so thatit only affects the verification match scores of enrollees withunreliable biometric features, the overall behavior of a biometricsystem may remains unaffected for those enrollees having biometricfeatures that are more reliable. Further, these adjustments to abiometric system for unreliable users can be minimal.

In general, the embodiments described herein may involve one or more ofthe following phases or processes: (1) offline training, (2),computation of a correction factor; and (3) application of thecorrection factor. The various aspects and features of these phases willnow be described in the following exemplary embodiments. While theexemplary embodiments described herein are described in the context of avoice or speech based biometric system, one of ordinary skill in the artshould be able to implement embodiments using other biometrics (e.g.,fingerprints, iris, etc.).

Offline Training

In offline training, a relationship between a persons biometric featuresand the reliability of biometrics identification system may bedeveloped. FIG. 1 is a density function graph 100 for an exemplarypopulation where match scores between feature vectors and referencetemplates of an exemplary population are plotted against standarddeviations of each sample in the population. The exemplary densityfunction graph 100 shown in FIG. 1 is based on speech data in a voicebiometrics application implementation. The x-axis 102 of the graph 100represents standard deviation values and the y-axis 104 represents matchscore values. In this example, the smaller the match score, the betterthe match between the feature vector and the reference template (i.e., asmaller match score indicates a better match). Each point (e.g., point106) in the density function graph 100 of FIG. 1 represents the plottingof a match score to standard deviation of a feature vector of a givenbiometric sample.

Based on the distribution of points in the graph of FIG. 1, thefollowing observations can be made: (1) the data is clustered towards acenter that represents a centroid of the distribution area; and (2) thematch score of a given sample tends to increase as the standarddeviation (SD) of the sample increases. As a result, it can be inferredfrom the graph 100 shown in FIG. 1 that users with smaller standarddeviations may have smaller match scores (i.e., better matches betweentheir feature vectors and reference template).

FIG. 2 is a density function graph 200 for an exemplary population wherematch scores between feature vectors and reference templates of anexemplary population are plotted against standard deviations of eachsample in the population. As in the graph 100 shown in FIG. 1, in thisgraph 200, the standard deviation values are represented along thex-axis 202 and the match score values are represented along the y-axis204.

In FIG. 2, a variety of trend lines 206, 208, 210 have been plotted forthe density function. These trend lines include a trend lines forlinear, quadratic and cubic equations (i.e., a linear trend line 206, aquadratic trend line 208 and a cubic trend line 210) to provide dataapproximations of the density function. As can be seen in this exemplaryimplementation, all three approximations are fairly straight. Therefore,in one embodiment, a simple line equation (such as e.g., trend line 206)can be used to represent the trend followed in a match score versusstandard deviation density function graph.

FIG. 3 is flowchart of a training process 300 for an biometric system inaccordance with one embodiment. This training process 300 may beperformed offline. In operation 302, biometric data for a test set ofvalid users may be obtained. As shown by the “No” path of decision 304,the biometric data for each user in the test set may then be subjectedto the following iterative set of operations (operations 306-314).

In operation 306, a feature vector comprising one or more featurecoefficients may be extracted from the biometric sample of a user. Inoperation 308, the standard deviation of each feature coefficient maythen be calculated from which a mean of standard deviation for thefeature vector may be calculated. The user may then be enrolled in thebiometric system in operation 310. In operation 312, a verificationmatch score (also referred to as a verification score) for the user maybe calculated from a verification sample of the user (and return path to304 may then be taken).

After the biometric data from the last user in the set has been subjectto operations 306-314, the “Yes” path of decision 304 may be followed tooperation 316. In operation 316, a scatter graph (i.e., density functiongraph) of the standard deviations versus the verification match scoresmay be generated using the standard deviations calculated in operation314 and the verification match scores calculated in operation 312.

From the density function graph, a centroid for the distribution area ofthe plotted points may be calculated in operation 318 and stored in acentroid data store 320. The centroid may be represented in terms of itsx- and y-axis values (i.e., a centroid standard deviation value and acentroid match score value).

In operation 322, a trend line of the density function generated inoperation 316 may then be derived. As shown in FIG. 3, the derived trendline may comprise a linear trend line that is a linear approximation ofthe data and have the form: score=m*sdev+c. The trend line generated inoperation 322 may then be stored in a line representation data store 324(e.g., a database).

Computing a Correction Factor

During enrollment, the trend line relationship developed for a densityfunction graph may be used to compute a correction factor that may beused in a biometric verification process. FIG. 4 is a flowchart of aprocess 400 for computing such a correction factor in accordance with anexemplary embodiment. In operation 402, a biometric sample (i.e.,feature data) of an end user (i.e., a potential enrollee). In operation404, a feature vector is obtained from the biometric sample. Thestandard deviation of each coefficient of the feature vector may becalculated and used to derive a mean of standard deviation for thefeature vector (also referred to as the standard deviation of thefeature vector) in operation 406.

With reference to decision 408, information about a centroid of apre-calculated density function of match scores to standard deviationsmay be obtained from a centroid data store 410 (e.g., a database). Theinformation about the centroid may include a centroid match score value(e.g., cy of centroid (cx, cy)) and a centroid standard deviation value(e.g., cx of centroid (cx, cy)). In decision 408, the standard deviationof the feature vector obtained in operation 406 may be compared to thecentroid's standard deviation (i.e., cx). If the standard deviation ofthe feature vector is less than the standard deviation value of thecentroid (i.e., to the left of the centroid in the density functiongraph), then no correction of the match score for the subject may bedeemed needed in the biometric system and the subject may be enrolledinto the biometric system in operation 412 (via the “Yes” path fromdecision 408).

If, on the other hand, the standard deviation of the feature vectorobtained in operation 406 is greater than the standard deviation valueof the centroid (i.e., to the right of the centroid in the densityfunction graph), then the “No” path from decision 408 may be followed inorder to calculate a correction factor for the potential enrollee.

Following the “No” path, a determination may be made in operation 414 todetermine whether the value of the standard deviation of the featurevector calculated in operation 406 exceeds a maximum value. If thestandard deviation is determined to exceed the maximum value, then thestandard deviation value for the feature vector may be set (i.e.,reduced or bound) to the maximum value in operation 414.

In one embodiment, the maximum value may, for example, correspond to astandard deviation value on the right side of the score versus standarddeviation density functional graph at which approximately about 5% toabout 20% (and preferably about 10%) of the total number of subjects(i.e., users) are located to the right of the value. Setting a maximumvalue for the standard deviation may be carried out in order to helpavoid spurious corrections due to outliers with very large standarddeviation values.

In operation 416, the value of the standard deviation of the featurevector computed in operation 406 (or modified value of the standarddeviation per operation 414) may then be used to derive a correctionfactor for the feature vector. In one embodiment, the correction factormay be derived by applying the standard deviation value (or modifiedstandard deviation value) to a line representation algorithm of thecentroid of the pre-calculated density function of match scores tostandard deviations. As previously described, in one embodiment, theline representation algorithm may be linear (e.g., a linear trend line),for example, and be represented as: score=m*sdev+c, where m is the slopeof the line, “sdev” is the standard deviation and “c” is a constantdefined at the intersection of the line to the match score axis of thedensity function graph. The line representation algorithm used inoperation 416 may be obtained from a centroid line representation datastore 418 that, in one embodiment, can comprise at least a portion ofthe centroid data store 410. The value output from operation 416 may beused as the correction factor for the given feature vector and stored ina correction factor data store 420. In one exemplary embodiment, thevalue of the correction factor may be derived from the algorithm:corr=cy−score, where “corr” is the correction factor, “cy” is thecentroid match score (i.e., the match score of centroid (cx, cy)) and“score” is the line representation.

Thus, in accordance with an exemplary embodiment, the followingillustrative pseudocode sets forth an illustrative process for derivinga correction score:

score = 0.63 * sdev + 1.4; corr = yc-score; where: “score” is the linerepresentation of the centroid of the density function; “sdev” is thestandard deviation of the feature vector or the maximum standarddeviation value if the standard deviation of the feature vector exceedsthe maximum standard deviation value; “corr” is the correction factor;and “yc” is the a centroid match score value (e.g., cy of centroid (cx,cy)).

Applying the Correction Factor

During verification, the match score of a user may be corrected with theapplied correction depending on the reliability of the particular user'sbiometric. This way, the user's corrected score can serve as a matchscore adapted to the characteristics of the user's own voice or otherbiometric. As previously described with reference to FIG. 4, thecorrection value itself may be determined during the enrollment process.The application of a correction factor to a user's match score may beaccomplished according to a process such as that set forth in FIG. 5.

FIG. 5 is a flowchart of a biometric verification process 500 in which acorrection factor may be applied in accordance with an exemplaryembodiment. In operation 502, feature vectors are obtained frombiometric data input by a user claiming an identity (i.e., a“claimant”). In operation 504, a match score (or “verification score”)may be calculated using the obtained feature vectors and, for example, areference template associated with the claimed identity. If the claimedidentity has a correction factor associated with it, then in operation506, the correction factor (also referred to as “Corr”) may be obtainedfrom a correction factor data store 508 and added to the match score toobtain an adjusted match score for the claimant (e.g., adjusted matchscore=match score+correction factor). The adjusted match score may thenbe used by a biometrics decision module in operation 510 for decidingwhether to accept or reject the claimant as the claimed identity.

The effect on decision making in a biometric verification system as aresult of the application of a correction factor to a match score can beexplained with reference to the exemplary density function graph 600 ofFIG. 6. In the density function graph 600 shown in FIG. 6, each point(e.g., point 602) represents a claimant in an exemplary biometricverification system with each point indicating the match score andstandard deviation of the given claimant.

A horizontal boundary line 604 extending across the density functiongraph 600 represents an illustrative threshold match score of theexemplary biometric verification system. The value of threshold matchscore (and thus the location of the boundary line 604 on the densityfunction graph 600) is dependent of the specific biometric verificationsystem and may be set to meet the needs of the particularimplementation. The boundary line 604 divides the density function graphinto upper and lower areas 606, 608. In the exemplary biometricverification system, claimants whose points are located in the upperarea 606 are designated as imposters (i.e., an imposter zone) whileclaimants having points located in the lower area 608 are designated asvalid subjects (i.e., a valid user zone).

As shown in FIG. 6, two claimants represented by the circled points arelocated close to the boundary line. Normally, claimant “1” would alwaysbe rejected (i.e., an imposter) by the exemplary biometric system. Withrespect to claimant “2,” small variations in the voice sample ofclaimant “2” could cause the match score for claimant “2” to becomelarger and cross over the boundary line into the upper area and, as aresult, the exemplary biometric system would frequently reject claimant“2” as an imposter.

By implementing the exemplary biometric verification system so that itcan apply a correction factor to the match scores of claimants near theboundary line such as claimants “1” and “2,” these claimants can beaccepted as valid subjects by the biometric verification system. Theapplication of a correction factor to the match scores of claimants “1”and “2” is represented by the downwards arrows. As represented by thesearrows, the application of a correction factor to the match scores ofclaimants “1” and “2” effectively shifts the match scores downwardsbelow the boundary line so that both claimants “1” and “2” would be morelikely to be accepted as valid subjects by the biometric verificationsystem.

FIG. 7 illustrates an exemplary hardware configuration of a computer 700having a central processing unit 702, such as a microprocessor, and anumber of other units interconnected via a system bus 704. The computer700 shown in FIG. 5 includes a Random Access Memory (RAM) 706, Read OnlyMemory (ROM) 708, an I/O adapter 710 for connecting peripheral devicessuch as, for example, disk storage units 712 and printers 714 to the bus704, a user interface adapter 716 for connecting various user interfacedevices such as, for example, a keyboard 718, a mouse 720, a speaker722, a microphone 724, and/or other user interface devices such as atouch screen or a digital camera to the bus 704, a communication adapter726 for connecting the computer 700 to a communication network 728(e.g., a data processing network) and a display adapter 730 forconnecting the bus 704 to a display device 732. The computer may utilizean operating system such as, for example, a Microsoft Windows operatingsystem (O/S), a Macintosh O/S, a Linux O/S and/or a UNIX O/S. Those ofordinary skill in the art will appreciate that embodiments may also beimplemented on platforms and operating systems other than thosementioned. One of ordinary skilled in the art will also be able tocombine software with appropriate general purpose or special purposecomputer hardware to create a computer system or computer sub-system forimplementing various embodiments described herein.

Embodiments of the present invention may also be implemented usingcomputer program languages such as, for example, ActiveX, Java, C, andthe C++ language and utilize object oriented programming methodology.Any such resulting program, having computer-readable code, may beembodied or provided within one or more computer-readable media, therebymaking a computer program product (i.e., an article of manufacture). Thecomputer readable media may be, for instance, a fixed (hard) drive,diskette, optical disk, magnetic tape, semiconductor memory such asread-only memory (ROM), etc., or any transmitting/receiving medium suchas the Internet or other communication network or link. The article ofmanufacture containing the computer code may be made and/or used byexecuting the code directly from one medium, by copying the code fromone medium to another medium, or by transmitting the code over anetwork.

In accordance with the foregoing, embodiments of a process for enrollinga subject in a biometric verification system may be implemented. In oneimplementation, a biometric sample may be captured from a subjectrequesting enrollment into a biometric system. From the capturedbiometric sample, at least one feature vector may be extracted with thefeature vector comprising one or more coefficients. A standard deviationmay be calculated for each of the coefficients of the feature vector. Asan option, a mean standard deviation may be calculated from thecoefficient standard deviations to represent the standard deviation forthe feature vector.

Next, a determination may be made as to whether the value of thecalculated standard deviation of the feature vector is greater than thevalue of a standard deviation represented by a centroid of a match scorevs. standard deviation density function of, for example, a samplepopulation. If the standard deviation of the feature vector isdetermined to be greater than the standard deviation of the centroid ofthe density function, then a correction factor may be derived for thesubject based on a linear representation of a trend line of the densityfunction and the standard deviation of the feature vector.

The correction factor may be derived from the difference between thevalue of a match score represented by the centroid of the match scorevs. standard deviation density function and a match score for thesubject derived from the trend line of the density function. In otherwords, the derivation of the correction factor may be performed bycalculating an “expected” match score for the feature vector of thesubject from the trend line of the density function and the standarddeviation of the feature vector of the subject. The value of the“expected” match score may be subtracted from the value of a match scorerepresented by the centroid of the match score vs. standard deviationdensity function with the output difference comprising the correctionfactor. The derivation of the correction factor may also include makinga determination as to whether the standard deviation of the featurevector exceeds a maximum threshold value for standard deviation values(e.g., a predefined maximum value), and if it does, then replacing thestandard deviation of the feature vector with the maximum standarddeviation threshold value. In such a situation (i.e., when the standarddeviation of the feature vector exceeds the maximum standard deviationthreshold value), the maximum standard deviation threshold value maythen be used instead of the originally calculated standard deviation ofthe feature vector with the trend line to derive the correction factorfor the subject. The derived correction factor may be stored in acorrection factor data store in a memory and/or memory device.

Embodiments of a process for verifying an identity of a claimant mayalso be implemented in accordance with the foregoing. In oneimplementation, a biometric capturing component may be used to capture abiometric sample from a claimant who claims a particular identity. Inone embodiment, the biometric sample may comprise a speech sample (i.e.,a vocal sample) made by the claimant. The captured biometric sample maythen be passed to an extraction component that can extract at least onefeature vector from the captured biometric sample.

The extracted feature may then be compared to a pre-generated referencetemplate associated with the claimed identity (i.e., the referencetemplate of an enrolled subject) to determine the degree (i.e., amount)of similarity between the extracted feature and the reference template.The degree of similarity may be represented by a match score that isoutput as a result of the comparison. In one implementation, thiscomparison may be carried out by a comparison component coupled to theextraction component.

Next, a determination may be made to determine whether the claimedidentity has a correction factor associated therewith. Thisdetermination may be accomplished by searching a correction factor datastore in which correction factors of enrolled subjects are stored. Thecorrection factor data store may reside in a memory and/or a memorydevice. If a correction factor for the claimed identity is found duringthis search, then it may be retrieved from the correction factor datastore and used to modifying the generated match score and thereby derivea modified match score. By modifying the match score with a correctionfactor, the degree of similarity between the feature vector and thereference template can be effectively increased for biometric purposes.In one embodiment, the modification of the match score may be performedby adding the correction factor to the match score. In implementationswhere lower match scores indicate a greater the degree of similarity(e.g., a match score of 2 indicates a greater match than a match scoreof 3) between the feature vector and the reference template, thecorrection factor may have a negative value so that the match score islowered in value by the addition of the correction factor.

A decision component may then compare either the modified match score(if the claimed identity is determined to have a correction factor) orthe unmodified match score (if the claimed identity is determined not tohave a correction factor) to a decision threshold value to determinewhether to accept the claimant as the claimed identity. If the value ofthe match score/modified match score of the claimant exceeds thedecision threshold value, then the claimant may be rejected (i.e.,classified as an imposter) by the biometric verification system.

The following references are hereby incorporated by reference herein intheir entirety: A. K. Jain, A. Ross and S. Prabhakar, “An Introductionto Biometric Recognition,” IEEE Transactions on Circuits and Systems forVideo Technology, Special Issue on Image- and Video-Based Biometrics,Vol. 14, No. 1, pp. 4-20, January 2004; and Ruud M. Bolle, SharathPankanti, Nalini K. Ratha, “Evaluation Techniques for Biometric-BasedAuthentication Systems (FRR),” IBM Computer Science Research Report RC21759, 2000.

Based on the foregoing specification, various embodiments may beimplemented using computer programming or engineering techniquesincluding computer software, firmware, hardware or any combination orsubset thereof. Any such resulting program—having computer-readablecode—may be embodied or provided in one or more computer-readable media,thereby making a computer program product (i.e., an article ofmanufacture) implementation of one or more embodiments described herein.The computer readable media may be, for instance, a fixed drive (e.g., ahard drive), diskette, optical disk, magnetic tape, semiconductor memorysuch as for example, read-only memory (ROM), flash-type memory, etc.,and/or any transmitting/receiving medium such as the Internet and/orother communication network or link. An article of manufacturecontaining the computer code may be made and/or used by executing thecode directly from one medium, by copying the code from one medium toanother medium, and/or by transmitting the code over a network. Inaddition, one of ordinary skill in the art of computer science may beable to combine the software created as described with appropriategeneral purpose or special purpose computer hardware to create acomputer system or computer sub-system embodying embodiments or portionsthereof described herein.

While various embodiments have been described, they have been presentedby way of example only, and not limitation. Thus, the breadth and scopeof any embodiment should not be limited by any of the above describedexemplary embodiments, but should be defined only in accordance with thefollowing claims and their equivalents.

1. A method, comprising: extracting a feature from a biometric samplecaptured from a subject requesting enrollment; calculating a standarddeviation for the feature using a microprocessor, the standard deviationcalculated from the biometric sample and at least one other biometricsample; determining whether the standard deviation of the feature isgreater than a standard deviation of a centroid of a density function;and deriving a correction factor for the subject based on a trend lineof the density function if the standard deviation of the feature isgreater than the standard deviation of the centroid.
 2. The method ofclaim 1, wherein the density function comprises a match score vs.standard deviation density function of a sample population.
 3. Themethod of claim 1, wherein the correction factor is further derived fromthe difference between a mean score obtained from the centroid and ascore for the subject derived from the trend line.
 4. The method ofclaim 1, further comprising determining whether the standard deviationof the feature exceeds a threshold value, and using the threshold valuein place of the standard deviation of the feature to derive thecorrection factor for the subject if the standard deviation of thefeature exceeds the threshold value.
 5. The method of claim 1, furthercomprising storing the correction factor in correction data store. 6.The method of claim 1, wherein the biometric sample comprises speech.